Kurtosis

INTRODUCTION The dataset used for the project is the German credit dataset that consists of customers’ financial and credit information and the resulting classification of customers as “good” or “bad” credit risks. This is a well-known publicly available dataset containing observations on 20 variables of 1000 past applicants of which 700 are classified as “good” credit risk and 300 are classified as “bad” credit risk. This report lists the detailed steps involved in developing a credit scoring model

descriptive statistics, histograms, stem-and-leaf plots, and the tests for normality that you obtained from your analyses (see box to check in “Plots” when using Explore to analyze descriptive statistics of your data). Considering the skewness and kurtosis values, as well as the Shapiro-Wilk’s results (preferred for small sample sizes), did the distribution of scores on either of the dependent variables violate the assumption of

mean, within this sample the median is 58.5 the mode is 58 which is greater than the mean of 57.62, the tail would be pointed toward the left (Salkind, 2013). The kurtosis value is -0.65102 and this describes how peak or flat the curve is from the normal distribution curve which is described as mesokurtic (Salkind, 2013). The kurtosis has a large negative value which is representative of a flatter curve also know as playkurtic (Salkind, 2013). The average deviation of scores from the sample mean

the risk free rate used is 3 months UKGBILL corresponding to the same time span. This sample was chosen based on the The summary statistics used which explains the statistical properties of the data are mean, median, standard deviation, skewness, kurtosis and test for normal distribution of the data. The mean and the median measure the central tendency of the financial data which shows the location or the clustering of the data around a certain central value. The standard deviation measures the dispersion

The Effect of Color on Testing Results Introduction Our experiment focused on testing scores. We wanted to see whether the color of ink that was used on a test would affect a student’s performance on that test. We chose to conduct this experiment in the Sterling C. Evans Library to get a wide variety of students to take the tests since it is the largest and most central library on campus. We felt that this way the best way to attempt to limit the other variables that could affect our results. Our

The first step is to access the data, which can be found at Sports Reference LLC (2000-2012). Then the data is tabulated columnwise, here into Microsoft Excel but that is not necessary. Double checking against Yahoo! Sports (2012) verifies the data is correct, quickly by counting total number and then by checking that the 150th and 180th scores are the same, which they are. What this checking accomplishes is to reveal "n," the number of entries, which will be necessary describing these results. This

Understanding and Exploring Assumptions Assumptions are important, but not in the way; many would think. The term assumption sometimes is associated with the word guess, but that’s not the meaning as it concerned statistics’ to ensure that your data. In the area of statistics ',’ the word assumption signifies completeness of the data collected (Triola, 2008) . The assumption aids the researcher in verifying the data because if the data does not meet the requirement, the researcher must, then treat

This report consists of the findings from tests run to assess both the normality and difference of means for the variables used in the We Read to Succeed community initiative in Harrisonburg-Rockingham County. This report also presents the findings from the proposed research questions, including: 1) Do mentor reported reading skills improve over time? 2) How does mean mentor reported strength of relationship at baseline vary by gender? How does it vary after 12 months by gender? 3) What affect do

Minimum Median Maximum Skewness Kurtosis APPOINTMENTS 100 28.780 4.378 19.000 29.000 39.000 0.04 -0.16 The shape of the histogram for the variable “APPOINTMENTS” depicts that the distribution of that variable is approximately normal with (M = 28.78, SD = 4.378). The median (29.00) which is almost coincides with the mean (28.78) and the coefficient of skewness (.04) is almost same as ‘0’, also confirms that the distribution is symmetric. The coefficient of kurtosis (–.16), close to ‘0’ specifies that

of Contents Introduction 3 Frequency Distribution of Customers based on their Ages 4 Measures of Central Tendency 5 The Arithmetic Mean 5 The Median 5 The Mode 5 Probability Distribution and Probability Density 6 Skewness of the Age Distribution 8 Kurtosis of the Histogram 10 Conclusion and Recommendation 13 References 14 Introduction The following report establishes the buying habits of the customers and determines what motivates them to come to our business. From research done elsewhere,